Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and m...
Likelihood-based marginalized models using random effects have become popular for analyzing longitud...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
Most of the available multivariate statistical models dictate on fitting different parameters for th...
Most of the available multivariate statistical models dictate on fitting different parameters for th...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
Longitudinal data arise when subjects are followed over time. This type of data is typically depende...
We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) ...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
In the health and social sciences, longitudinal data have often been analyzed without taking into ac...
Data with multiple responses is ubiquitous in modern applications. However, few tools are available ...
Multivariate longitudinal data frequently arise in biomedical applications; however, their analyses ...
Generalized linear models with random effects and/or serial dependence are commonly used to analyze ...
Likelihood-based marginalized models using random effects have become popular for analyzing longitud...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
Most of the available multivariate statistical models dictate on fitting different parameters for th...
Most of the available multivariate statistical models dictate on fitting different parameters for th...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
Longitudinal data arise when subjects are followed over time. This type of data is typically depende...
We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) ...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
In the health and social sciences, longitudinal data have often been analyzed without taking into ac...
Data with multiple responses is ubiquitous in modern applications. However, few tools are available ...
Multivariate longitudinal data frequently arise in biomedical applications; however, their analyses ...
Generalized linear models with random effects and/or serial dependence are commonly used to analyze ...
Likelihood-based marginalized models using random effects have become popular for analyzing longitud...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...